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Actuation Confirmation and Negation via Facial-Identity and -Expression Recognition

机译:通过面部身份和表情识别进行驱动确认和否定

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This paper presents the implementation of a facial-identity and -expression recognition mechanism that confirms or negates physical and/or computational actuations in an intelligent built-environment. Said mechanism is built via Google Brain's TensorFlow (as regards facial identity recognition) and Google Cloud Platform's Cloud Vision API (as regards facial gesture recognition); and it is integrated into the ongoing development of an intelligent built-environment framework, viz., Design-to-Robotic-Production & -Operation (D2RP&O), conceived at Delft University of Technology (TUD). The present work builds on the inherited technological ecosystem and technical functionality of the Design-to-Robotic-Operation (D2RO) component of said framework; and its implementation is validated via two scenarios (physical and computational). In the first scenario-and building on an inherited adaptive mechanism-if building-skin components perceive a rise in interior temperature levels, natural ventilation is promoted by increasing degrees of aperture. This measure is presently confirmed or negated by a corresponding facial expression on the part of the user in response to said reaction, which serves as an intuitive override / feedback mechanism to the intelligent building-skin mechanism's decision-making process. In the second scenario-and building on another inherited mechanism-if an accidental fall is detected and the user remains consciously or unconsciously collapsed, a series of automated emergency notifications (e.g., SMS, email, etc.) are sent to family and/or care-takers by particular mechanisms in the intelligent built-environment. The precision of this measure and its execution are presently confirmed by (a) identity detection of the victim, and (b) recognition of a reflexive facial gesture of pain and/or displeasure. The work presented in this paper promotes a considered relationship between the architecture of the built-environment and the Information and Communication Technologies (ICTs) embedded and/or deployed.
机译:本文介绍了一种面部身份和表情识别机制的实现,该机制在智能建筑环境中确认或消除了物理和/或计算驱动。所述机制是通过Google Brain的TensorFlow(关于面部身份识别)和Google Cloud Platform的Cloud Vision API(关于面部手势识别)构建的;并且已将其集成到正在进行的智能建筑环境框架开发中,即由代尔夫特理工大学(TUD)构思的机器人生产和运营设计(D2RP&O)。本工作建立在继承的技术生态系统和所述框架的“设计为机器人操作”(D2RO)组件的技术功能之上;并通过两种方案(物理和计算)验证了其实现。在第一种情况下-并在继承的自适应机制上进行构建-如果建筑物的皮肤组件感知到内部温度水平升高,则通过增加开孔程度来促进自然通风。当前,响应于所述反应,通过用户方面的相应面部表情来确认或否定该措施,其用作智能建筑皮肤机制的决策过程的直观的超越/反馈机制。在第二种情况下(并建立在另一个继承的机制上),如果检测到意外跌倒并且用户仍然有意识或无意识地崩溃,则会向家人和/或家庭发送一系列自动紧急通知(例如SMS,电子邮件等)在智能建筑环境中通过特定机制来照料看护者。目前,该措施的精确性及其执行方式是通过(a)对受害者的身份检测,以及(b)对疼痛和/或不满的反射性面部姿势的识别来确认的。本文提出的工作促进了内置环境的体系结构与嵌入和/或部署的信息和通信技术(ICT)之间的考虑的关系。

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